SF.SIR {sufficientForecasting} | R Documentation |
Sliced inverse regression for sufficient forecasting
Description
Sliced inverse regression for sufficient forecasting
Usage
SF.SIR(
y,
X,
newX = NULL,
type = "LM",
K = "default",
L = 1,
discretization = TRUE,
nslices = 10
)
Arguments
y |
Response, T by 1 matrix |
X |
Predictors, p by T matrix |
newX |
New predictors, a vector contains p entries (or |
type |
|
K |
The number of common factors (default = obtained
by |
L |
The number of predictive indices, L is required to be no greater than K (default = 1) |
discretization |
Hyperparameter in SIR (default = |
nslices |
Hyperparameter in SIR (default = 10) |
Value
Out-of-sample forecast for newX
; or in-sample forecast for the last
observed data point if newX
is NULL
References
Fan, J., Xue, L. and Yao, J. (2017), Sufficient forecasting using factor models, Journal of econometrics 201(2), 292–306.
Yu, X., Yao, J. and Xue, L. (2022), Nonparametric estimation and conformal inference of the sufficient forecasting with a diverging number of factors, Journal of Business & Economic Statistics 40(1), 342–354.
Examples
utils::data(dataExample,package = "sufficientForecasting")
SF.SIR(dataExample$y,dataExample$X,type = "LLM")